title: Gaussian mixture modeling in stroke patients' rehabilitation EEG data analysis.
abstract: Traditional 2-class Motor Imagery (MI) Electroencephalography (EEG) classification approaches like Common Spatial Pattern (CSP) and Support Vector Machine (SVM) usually underperform when processing stroke patients' rehabilitation EEG which are flooded with unknown irregular patterns. In this paper, the classical CSP-SVM schema is improved and a feature learning method based on Gaussian Mixture Model (GMM) is utilized for depicting patients' imagery EEG distribution features. We apply the proposed modeling program in two different modules of our online BCI-FES rehabilitation platform and achieve a relatively higher discrimination accuracy. Sufficient observations and test cases on patients' MI data sets have been implemented for validating the GMM model. The results also reveal some working mechanisms and recovery appearances of impaired cortex during the rehabilitation training period.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Ye
authors.initials: Y
authors.lastname: Liu
authors.forename: Jianyi
authors.initials: J
authors.lastname: Liang
authors.forename: Jianting
authors.initials: J
authors.lastname: Cao
authors.forename: Liqing
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000465:Algorithms; D001921:Brain; D062207:Brain-Computer Interfaces; D004558:Electric Stimulation; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009043:Motor Activity; D016011:Normal Distribution; D013223:Statistics as Topic; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation